{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "lesson3-rsna-pneumonia-detection-mdai-client-lib.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "FdivV_kX8_PN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Intro to deep learning for medical imaging by [MD.ai](https://www.md.ai)\n",
        "\n",
        "## Lesson 3. RSNA Pneumonia Detection Challenge (MD.ai API)\n",
        "\n",
        "The [Radiological Society of North America](http://www.rsna.org/) Pneumonia Detection Challenge: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge\n",
        "\n",
        "This notebook covers the basics of parsing the competition dataset, training using a detector basd on the [Mask-RCNN algorithm](https://arxiv.org/abs/1703.06870) for object detection and instance segmentation.  \n",
        "\n",
        "This notebook is developed by [MD.ai](https://www.md.ai), the platform for medical AI. \n",
        "This notebook requires Google's [TensorFlow](https://www.tensorflow.org/) machine learning framework.\n",
        "\n",
        "- MD.ai annotator project URL for this dataset: https://public.md.ai/annotator/project/LxR6zdR2/workspace\n",
        "- MD.ai python client libray URL: https://github.com/mdai/mdai-client-py\n",
        "- MD.ai documentation URL: https://docs.md.ai/\n",
        "\n",
        "**Intro to deep learning for medical imaging lessons**\n",
        "\n",
        "- Lesson 1. Classification of chest vs. adominal X-rays using TensorFlow/Keras [Github](https://github.com/mdai/ml-lessons/blob/master/lesson1-xray-images-classification.ipynb) [Annotator](https://public.md.ai/annotator/project/PVq9raBJ)\n",
        "\n",
        "- Lesson 2. Lung X-Rays Semantic Segmentation using UNets. [Github](https://github.com/mdai/ml-lessons/blob/master/lesson2-lung-xrays-segmentation.ipynb)\n",
        "[Annotator](https://public.md.ai/annotator/project/aGq4k6NW/workspace) \n",
        "\n",
        "- Lesson 3. RSNA Pneumonia detection using Kaggle data format [Github](https://github.com/mdai/ml-lessons/blob/master/lesson3-rsna-pneumonia-detection-kaggle.ipynb) [Annotator](https://public.md.ai/annotator/project/LxR6zdR2/workspace) \n",
        "  \n",
        "- Lesson 3. RSNA Pneumonia detection using MD.ai python client library [Github](https://github.com/mdai/ml-lessons/blob/master/lesson3-rsna-pneumonia-detection-mdai-client-lib.ipynb) [Annotator](https://public.md.ai/annotator/project/LxR6zdR2/workspace)\n",
        "\n",
        "*Copyright 2018 MD.ai, Inc.   \n",
        "Licensed under the Apache License, Version 2.0*"
      ]
    },
    {
      "metadata": {
        "id": "3VZvE3gX8_PP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# install dependencies not included by Colab\n",
        "# use pip3 to ensure compatibility w/ Google Deep Learning Images \n",
        "!pip3 install -q pydicom \n",
        "!pip3 install -q tqdm \n",
        "!pip3 install -q imgaug "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lVBBH_EF8_PU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import sys\n",
        "import random\n",
        "import math\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import json\n",
        "import pydicom\n",
        "from imgaug import augmenters as iaa\n",
        "\n",
        "%matplotlib inline "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0a1bLOhX8_PW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Import the `mdai` library\n",
        "\n",
        "On Colab, run the block below to install the `mdai` client library into your python environment."
      ]
    },
    {
      "metadata": {
        "id": "cYvDgipk8_PX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "28c7fa59-b481-4b40-dfe1-3e2f49cbb9a5"
      },
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --quiet mdai\n",
        "import mdai\n",
        "mdai.__version__"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0.0.5'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "WZQjgjnK8_Pb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Root directory of the project\n",
        "ROOT_DIR = os.path.abspath('./lesson3-data')\n",
        "\n",
        "# Directory to save logs and trained model\n",
        "MODEL_DIR = os.path.join(ROOT_DIR, 'logs')\n",
        "\n",
        "if not os.path.exists(ROOT_DIR):\n",
        "    os.makedirs(ROOT_DIR)\n",
        "os.chdir(ROOT_DIR)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FyzgNwkn8_Pd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Install Matterport's Mask-RCNN model from github.\n",
        "See the [Matterport's implementation of Mask-RCNN](https://github.com/matterport/Mask_RCNN)."
      ]
    },
    {
      "metadata": {
        "id": "WlPtsVgt8_Pe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "9221b2f6-51a5-4a23-e9cc-9d7588221a41"
      },
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/matterport/Mask_RCNN.git\n",
        "os.chdir('Mask_RCNN')\n",
        "!pip install -q -r requirements.txt\n",
        "!python setup.py -q install"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Mask_RCNN'...\n",
            "remote: Counting objects: 775, done.\u001b[K\n",
            "remote: Total 775 (delta 0), reused 0 (delta 0), pack-reused 774\u001b[K\n",
            "Receiving objects: 100% (775/775), 107.33 MiB | 20.46 MiB/s, done.\n",
            "Resolving deltas: 100% (446/446), done.\n",
            "WARNING:root:Fail load requirements file, so using default ones.\n",
            "zip_safe flag not set; analyzing archive contents...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dzlegKck8_Pj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Import Mask RCNN\n",
        "sys.path.append(os.path.join(ROOT_DIR, 'Mask_RCNN'))  # To find local version of the library\n",
        "from mrcnn.config import Config\n",
        "from mrcnn import utils\n",
        "import mrcnn.model as modellib\n",
        "from mrcnn import visualize\n",
        "from mrcnn.model import log"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RrVjmqtJ8_Po",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Create an `mdai` client\n",
        "\n",
        "The mdai client requires an access token, which authenticates you as the user. To create a new token or select an existing token, navigate to the \"Personal Access Tokens\" tab on your user settings page at the specified MD.ai domain (e.g., public.md.ai).\n",
        "\n",
        "**Important: keep your access tokens safe. Do not ever share your tokens.**"
      ]
    },
    {
      "metadata": {
        "id": "XB28YDKw8_Pq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "68eed3fe-121f-4966-f53d-bd734025e262"
      },
      "cell_type": "code",
      "source": [
        "mdai_client = mdai.Client(domain='public.md.ai', access_token=\"\")"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully authenticated to public.md.ai.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "z0i1JLTF8_Pu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Clone the Annotator Project\n",
        "\n",
        "The annotator project URL for this dataset is https://public.md.ai/annotator/project/LxR6zdR2/workspace. \n",
        "\n",
        "To add annotations to the cloned project, you need to clone the project first. \n",
        "\n",
        "First, navigate to the original project URL (above), click on \"Clone Project\" button. \n",
        "\n",
        "![GitHub Logo](https://raw.githubusercontent.com/mdai/ml-lessons/master/images/kaggle-project-clone-small.gif)\n"
      ]
    },
    {
      "metadata": {
        "id": "Zl509Hs38_Pu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Define project\n",
        "\n",
        "Define a project you have access to by passing in the project id. The project id can be found in the URL in the following format: `https://public.md.ai/annotator/project/{project_id}`.\n",
        "\n",
        "For example, `project_id` would be `XXXX` for `https://public.md.ai/annotator/project/XXXX`.\n",
        "\n",
        "Specify optional `path` as the data directory (if left blank, will default to current working directory)."
      ]
    },
    {
      "metadata": {
        "id": "uXUv3t_98_Pv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "p = mdai_client.project('LxR6zdR2', path='./lesson3-data')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PXiRWnxX8_Pz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Prepare data"
      ]
    },
    {
      "metadata": {
        "id": "EDecTsz28_P0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "aeec82e1-7a0b-477f-fd37-6ec1a5a7b4df"
      },
      "cell_type": "code",
      "source": [
        "p.show_label_groups()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label Group, Id: G_R28gN5, Name: Default group\n",
            "\tLabels:\n",
            "\tId: L_ylR0L8, Name: No Lung Opacity\n",
            "\tId: L_DlqEAl, Name: Lung Opacity\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gB22GyWU8_P4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Set label ids\n",
        "\n",
        "Selected label ids must be explicitly set by `Project#set_label_ids` method in order to prepare datasets."
      ]
    },
    {
      "metadata": {
        "id": "ChuEtYTz8_P4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "67326c39-cc58-414b-9189-f9337e24a9f2"
      },
      "cell_type": "code",
      "source": [
        "# this maps label ids to class ids\n",
        "labels_dict = {'L_ylR0L8':0, # background \n",
        "               'L_DlqEAl':1, # lung opacity \n",
        "              }\n",
        "\n",
        "print(labels_dict)\n",
        "p.set_labels_dict(labels_dict)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'L_ylR0L8': 0, 'L_DlqEAl': 1}\n",
            "None\n",
            "bbox\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dFJRPTL18_P8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "a198f18a-ab68-4eda-d28e-af888206d35e"
      },
      "cell_type": "code",
      "source": [
        "p.show_datasets()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Datasets:\n",
            "Id: D_ao3XWQ, Name: stage 1 train\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1K-f6JdP8_QB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset = p.get_dataset_by_id('D_ao3XWQ')\n",
        "dataset.prepare()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xBIVY6IL8_QE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "833066ff-317a-4c9d-f38b-1e7c9269b16d"
      },
      "cell_type": "code",
      "source": [
        "dataset.show_classes()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label id: L_ylR0L8, Class id: 0, Class text: No Lung Opacity\n",
            "Label id: L_DlqEAl, Class id: 1, Class text: Lung Opacity\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WN35AoRi8_QK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "anns = dataset.get_annotations()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DAqhwKkH8_QN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Split the data into training and validation datasets"
      ]
    },
    {
      "metadata": {
        "id": "b0Q9DwBm8_QN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b7b26861-316c-433a-8f11-ee04e28a9742"
      },
      "cell_type": "code",
      "source": [
        "train_dataset, valid_dataset = mdai.common_utils.train_test_split(dataset)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Num of instances for training set: 23115, validation set: 2569\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "r3TPI1U78_QQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bc0eefa8-ef93-44c8-bb81-a2904a7e1543"
      },
      "cell_type": "code",
      "source": [
        "anns = dataset.get_annotations(labels_dict.keys(), verbose=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset contains 28989 annotations, filtered by label ids dict_keys(['L_ylR0L8', 'L_DlqEAl']).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WkU89vjJ8_QU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Mask-RCNN Config\n",
        "\n",
        "see [https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/config.py](https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/config.py)"
      ]
    },
    {
      "metadata": {
        "id": "Wl-YnHcL8_QU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        },
        "outputId": "afce6c03-862d-4e5f-f81c-6d98c75d2b6e"
      },
      "cell_type": "code",
      "source": [
        "# These parameters have been changed to reduce run-time per epoch at the expense \n",
        "# of training performance\n",
        "\n",
        "class DetectorConfig(Config):\n",
        "    \"\"\"Configuration for training pneumonia detection on the RSNA pneumonia dataset.\n",
        "    Overrides values in the base Config class.\n",
        "    \"\"\"\n",
        "    NAME = 'pneumonia'\n",
        "    GPU_COUNT = 1\n",
        "    IMAGES_PER_GPU = 16 # on aws, use 16\n",
        "    BACKBONE = 'resnet50'\n",
        "    NUM_CLASSES = 2  # background + 1 pneumonia classes\n",
        "    IMAGE_MIN_DIM = 64\n",
        "    IMAGE_MAX_DIM = 64\n",
        "    RPN_ANCHOR_SCALES = (32, 64)\n",
        "    TRAIN_ROIS_PER_IMAGE = 16\n",
        "    MAX_GT_INSTANCES = 3\n",
        "    DETECTION_MAX_INSTANCES = 3\n",
        "    DETECTION_MIN_CONFIDENCE = 0.9\n",
        "    DETECTION_NMS_THRESHOLD = 0.1\n",
        "    POST_NMS_ROIS_TRAINING = 200\n",
        "    RPN_TRAIN_ANCHORS_PER_IMAGE = 16\n",
        "    STEPS_PER_EPOCH = 100 \n",
        "    TOP_DOWN_PYRAMID_SIZE = 32\n",
        "     \n",
        "config = DetectorConfig()\n",
        "config.display()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Configurations:\n",
            "BACKBONE                       resnet50\n",
            "BACKBONE_STRIDES               [4, 8, 16, 32, 64]\n",
            "BATCH_SIZE                     16\n",
            "BBOX_STD_DEV                   [0.1 0.1 0.2 0.2]\n",
            "COMPUTE_BACKBONE_SHAPE         None\n",
            "DETECTION_MAX_INSTANCES        3\n",
            "DETECTION_MIN_CONFIDENCE       0.9\n",
            "DETECTION_NMS_THRESHOLD        0.1\n",
            "FPN_CLASSIF_FC_LAYERS_SIZE     1024\n",
            "GPU_COUNT                      1\n",
            "GRADIENT_CLIP_NORM             5.0\n",
            "IMAGES_PER_GPU                 16\n",
            "IMAGE_MAX_DIM                  64\n",
            "IMAGE_META_SIZE                14\n",
            "IMAGE_MIN_DIM                  64\n",
            "IMAGE_MIN_SCALE                0\n",
            "IMAGE_RESIZE_MODE              square\n",
            "IMAGE_SHAPE                    [64 64  3]\n",
            "LEARNING_MOMENTUM              0.9\n",
            "LEARNING_RATE                  0.001\n",
            "LOSS_WEIGHTS                   {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0}\n",
            "MASK_POOL_SIZE                 14\n",
            "MASK_SHAPE                     [28, 28]\n",
            "MAX_GT_INSTANCES               3\n",
            "MEAN_PIXEL                     [123.7 116.8 103.9]\n",
            "MINI_MASK_SHAPE                (56, 56)\n",
            "NAME                           pneumonia\n",
            "NUM_CLASSES                    2\n",
            "POOL_SIZE                      7\n",
            "POST_NMS_ROIS_INFERENCE        1000\n",
            "POST_NMS_ROIS_TRAINING         200\n",
            "ROI_POSITIVE_RATIO             0.33\n",
            "RPN_ANCHOR_RATIOS              [0.5, 1, 2]\n",
            "RPN_ANCHOR_SCALES              (32, 64)\n",
            "RPN_ANCHOR_STRIDE              1\n",
            "RPN_BBOX_STD_DEV               [0.1 0.1 0.2 0.2]\n",
            "RPN_NMS_THRESHOLD              0.7\n",
            "RPN_TRAIN_ANCHORS_PER_IMAGE    16\n",
            "STEPS_PER_EPOCH                100\n",
            "TOP_DOWN_PYRAMID_SIZE          32\n",
            "TRAIN_BN                       False\n",
            "TRAIN_ROIS_PER_IMAGE           16\n",
            "USE_MINI_MASK                  True\n",
            "USE_RPN_ROIS                   True\n",
            "VALIDATION_STEPS               50\n",
            "WEIGHT_DECAY                   0.0001\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bQwvhM7d8_QY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Mask-RCNN Dataset\n",
        "\n",
        "see [https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/utils.py](https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/utils.py)"
      ]
    },
    {
      "metadata": {
        "id": "oAj15YYF8_QY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class DetectorDataset(utils.Dataset):\n",
        "    \"\"\"Dataset class for training pneumonia detection on the RSNA pneumonia dataset.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, source_name, dataset, orig_height, orig_width):\n",
        "        super().__init__(self)\n",
        "        \n",
        "        # add classes \n",
        "        for k, v in dataset.classes_dict.items():\n",
        "            if v['class_id'] is not 0: \n",
        "                self.add_class(source_name, v['class_id'], v['class_text'])\n",
        "\n",
        "        # add images \n",
        "        img_ids = dataset.get_image_ids()\n",
        "        imgs_anns_dict = dataset.imgs_anns_dict\n",
        "        \n",
        "        #########################################################\n",
        "        # WARNING: Using only the first 1000 images to reduce \n",
        "        #          run-time for demonstration only \n",
        "        # To run all images, do: for i, fp in enumerate(img_ids):\n",
        "        #########################################################\n",
        "        for i, fp in enumerate(img_ids[:1000]):\n",
        "            annotations = imgs_anns_dict[fp]\n",
        "            self.add_image(source_name, image_id=i, path=fp, \n",
        "                           annotations=annotations, orig_height=orig_height, orig_width=orig_width)\n",
        "            \n",
        "    def image_reference(self, image_id):\n",
        "        info = self.image_info[image_id]\n",
        "        return info['path']\n",
        "\n",
        "    def load_image(self, image_id):\n",
        "        info = self.image_info[image_id]\n",
        "        fp = info['path']\n",
        "        ds = pydicom.read_file(\n",
        "            fp)\n",
        "        image = ds.pixel_array\n",
        "        # If grayscale. Convert to RGB for consistency.\n",
        "        if len(image.shape) != 3 or image.shape[2] != 3:\n",
        "            image = np.stack((image,) * 3, -1)\n",
        "        return image\n",
        "\n",
        "    def load_mask(self, image_id):\n",
        "        info = self.image_info[image_id]\n",
        "        annotations = info['annotations']\n",
        "        count = len(annotations)\n",
        "        if count == 0:\n",
        "            mask = np.zeros((info['orig_height'], info['orig_width'], 1), dtype=np.uint8)\n",
        "            assert(dataset.label_id_to_class_id(a['labelId']) == 0)\n",
        "            class_ids = np.zeros((1,), dtype=np.int32)\n",
        "        else:\n",
        "            mask = np.zeros((info['orig_height'], info['orig_width'], count), dtype=np.uint8)\n",
        "            class_ids = np.zeros((count,), dtype=np.int32)\n",
        "            for i, a in enumerate(annotations):\n",
        "                if a['data'] is not None:\n",
        "                    x = int(a['data']['x'])\n",
        "                    y = int(a['data']['y'])\n",
        "                    w = int(a['data']['width'])\n",
        "                    h = int(a['data']['height'])\n",
        "                    mask_instance = mask[:, :, i].copy()\n",
        "                    cv2.rectangle(mask_instance, (x, y), (x+w, y+h), 255, -1)\n",
        "                    mask[:, :, i] = mask_instance\n",
        "                    class_ids[i] = dataset.label_id_to_class_id(a['labelId'])\n",
        "        return mask.astype(np.bool), class_ids.astype(np.int32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Cw_yW5NR8_Qa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Training dataset\n",
        "source_name = 'pneumonia'\n",
        "dataset_train = DetectorDataset(source_name, train_dataset, 1024, 1024)\n",
        "dataset_train.prepare()\n",
        "\n",
        "# Validation dataset\n",
        "dataset_val = DetectorDataset(source_name, valid_dataset, 1024, 1024)\n",
        "dataset_val.prepare()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yTOUrxKv8_Qd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Display a few images"
      ]
    },
    {
      "metadata": {
        "id": "Oyt9sRT78_Qe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "b89b7278-48f0-4a3a-e92d-af0017731ad9"
      },
      "cell_type": "code",
      "source": [
        "# Load and display random samples\n",
        "image_id = random.choice(dataset_train.image_ids)\n",
        "image = dataset_train.load_image(image_id)\n",
        "mask, class_ids = dataset_train.load_mask(image_id)\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.imshow(image[:, :, 0], cmap='gray')\n",
        "plt.axis('off')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "masked = np.zeros(image.shape[:2])\n",
        "for i in range(mask.shape[2]):\n",
        "    masked += image[:, :, 0] * mask[:, :, i]\n",
        "plt.imshow(masked, cmap='gray')\n",
        "plt.axis('off')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 1023.5, 1023.5, -0.5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hvj5Pe/Nlz/Em+2HA21cFyvb7fZRnAEjBhMB8AbYQy2POivn1cNTH2Bj2cFytVioWi3rn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7Ev46N2u30nDUKWrhcGpLF/k33tZAP7GmyaJ+CQcEaS12AwULlcDjB2enqqarWa6JX7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\npaWlpaWlvaAl+EpLS0tLS0tLe0H7f98fmJZnifkNAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f0c69bcaf98>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "DMfjPgHe8_Qg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Training"
      ]
    },
    {
      "metadata": {
        "id": "rKgZXt808_Qh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = modellib.MaskRCNN(mode='training', config=config, model_dir=MODEL_DIR)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5c9_Afh68_Qj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "augmentation = iaa.SomeOf((0, 1), [\n",
        "    iaa.Fliplr(0.5),\n",
        "    iaa.Affine(\n",
        "        scale={\"x\": (0.8, 1.2), \"y\": (0.8, 1.2)},\n",
        "        translate_percent={\"x\": (-0.2, 0.2), \"y\": (-0.2, 0.2)},\n",
        "        rotate=(-25, 25),\n",
        "        shear=(-8, 8)\n",
        "    ),\n",
        "    iaa.Multiply((0.9, 1.1))\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TRqcDOty8_Ql",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## To get good results, you need to train for many epochs. "
      ]
    },
    {
      "metadata": {
        "id": "ArLlJnNP8_Ql",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2575
        },
        "outputId": "372c846a-c359-489c-ff92-693090817286"
      },
      "cell_type": "code",
      "source": [
        "# increase the number of epochs for (potentially) better trained model\n",
        "NUM_EPOCHS = 1\n",
        "\n",
        "# Train Mask-RCNN Model \n",
        "import warnings \n",
        "warnings.filterwarnings(\"ignore\")\n",
        "model.train(dataset_train, dataset_val, \n",
        "            learning_rate=config.LEARNING_RATE, \n",
        "            epochs=NUM_EPOCHS, \n",
        "            #augmentation=augmentation, # uncomment to enable augmentation\n",
        "            layers='all')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Starting at epoch 0. LR=0.001\n",
            "\n",
            "Checkpoint Path: /content/lesson3-data/logs/pneumonia20180827T1517/mask_rcnn_pneumonia_{epoch:04d}.h5\n",
            "Selecting layers to train\n",
            "conv1                  (Conv2D)\n",
            "bn_conv1               (BatchNorm)\n",
            "res2a_branch2a         (Conv2D)\n",
            "bn2a_branch2a          (BatchNorm)\n",
            "res2a_branch2b         (Conv2D)\n",
            "bn2a_branch2b          (BatchNorm)\n",
            "res2a_branch2c         (Conv2D)\n",
            "res2a_branch1          (Conv2D)\n",
            "bn2a_branch2c          (BatchNorm)\n",
            "bn2a_branch1           (BatchNorm)\n",
            "res2b_branch2a         (Conv2D)\n",
            "bn2b_branch2a          (BatchNorm)\n",
            "res2b_branch2b         (Conv2D)\n",
            "bn2b_branch2b          (BatchNorm)\n",
            "res2b_branch2c         (Conv2D)\n",
            "bn2b_branch2c          (BatchNorm)\n",
            "res2c_branch2a         (Conv2D)\n",
            "bn2c_branch2a          (BatchNorm)\n",
            "res2c_branch2b         (Conv2D)\n",
            "bn2c_branch2b          (BatchNorm)\n",
            "res2c_branch2c         (Conv2D)\n",
            "bn2c_branch2c          (BatchNorm)\n",
            "res3a_branch2a         (Conv2D)\n",
            "bn3a_branch2a          (BatchNorm)\n",
            "res3a_branch2b         (Conv2D)\n",
            "bn3a_branch2b          (BatchNorm)\n",
            "res3a_branch2c         (Conv2D)\n",
            "res3a_branch1          (Conv2D)\n",
            "bn3a_branch2c          (BatchNorm)\n",
            "bn3a_branch1           (BatchNorm)\n",
            "res3b_branch2a         (Conv2D)\n",
            "bn3b_branch2a          (BatchNorm)\n",
            "res3b_branch2b         (Conv2D)\n",
            "bn3b_branch2b          (BatchNorm)\n",
            "res3b_branch2c         (Conv2D)\n",
            "bn3b_branch2c          (BatchNorm)\n",
            "res3c_branch2a         (Conv2D)\n",
            "bn3c_branch2a          (BatchNorm)\n",
            "res3c_branch2b         (Conv2D)\n",
            "bn3c_branch2b          (BatchNorm)\n",
            "res3c_branch2c         (Conv2D)\n",
            "bn3c_branch2c          (BatchNorm)\n",
            "res3d_branch2a         (Conv2D)\n",
            "bn3d_branch2a          (BatchNorm)\n",
            "res3d_branch2b         (Conv2D)\n",
            "bn3d_branch2b          (BatchNorm)\n",
            "res3d_branch2c         (Conv2D)\n",
            "bn3d_branch2c          (BatchNorm)\n",
            "res4a_branch2a         (Conv2D)\n",
            "bn4a_branch2a          (BatchNorm)\n",
            "res4a_branch2b         (Conv2D)\n",
            "bn4a_branch2b          (BatchNorm)\n",
            "res4a_branch2c         (Conv2D)\n",
            "res4a_branch1          (Conv2D)\n",
            "bn4a_branch2c          (BatchNorm)\n",
            "bn4a_branch1           (BatchNorm)\n",
            "res4b_branch2a         (Conv2D)\n",
            "bn4b_branch2a          (BatchNorm)\n",
            "res4b_branch2b         (Conv2D)\n",
            "bn4b_branch2b          (BatchNorm)\n",
            "res4b_branch2c         (Conv2D)\n",
            "bn4b_branch2c          (BatchNorm)\n",
            "res4c_branch2a         (Conv2D)\n",
            "bn4c_branch2a          (BatchNorm)\n",
            "res4c_branch2b         (Conv2D)\n",
            "bn4c_branch2b          (BatchNorm)\n",
            "res4c_branch2c         (Conv2D)\n",
            "bn4c_branch2c          (BatchNorm)\n",
            "res4d_branch2a         (Conv2D)\n",
            "bn4d_branch2a          (BatchNorm)\n",
            "res4d_branch2b         (Conv2D)\n",
            "bn4d_branch2b          (BatchNorm)\n",
            "res4d_branch2c         (Conv2D)\n",
            "bn4d_branch2c          (BatchNorm)\n",
            "res4e_branch2a         (Conv2D)\n",
            "bn4e_branch2a          (BatchNorm)\n",
            "res4e_branch2b         (Conv2D)\n",
            "bn4e_branch2b          (BatchNorm)\n",
            "res4e_branch2c         (Conv2D)\n",
            "bn4e_branch2c          (BatchNorm)\n",
            "res4f_branch2a         (Conv2D)\n",
            "bn4f_branch2a          (BatchNorm)\n",
            "res4f_branch2b         (Conv2D)\n",
            "bn4f_branch2b          (BatchNorm)\n",
            "res4f_branch2c         (Conv2D)\n",
            "bn4f_branch2c          (BatchNorm)\n",
            "res5a_branch2a         (Conv2D)\n",
            "bn5a_branch2a          (BatchNorm)\n",
            "res5a_branch2b         (Conv2D)\n",
            "bn5a_branch2b          (BatchNorm)\n",
            "res5a_branch2c         (Conv2D)\n",
            "res5a_branch1          (Conv2D)\n",
            "bn5a_branch2c          (BatchNorm)\n",
            "bn5a_branch1           (BatchNorm)\n",
            "res5b_branch2a         (Conv2D)\n",
            "bn5b_branch2a          (BatchNorm)\n",
            "res5b_branch2b         (Conv2D)\n",
            "bn5b_branch2b          (BatchNorm)\n",
            "res5b_branch2c         (Conv2D)\n",
            "bn5b_branch2c          (BatchNorm)\n",
            "res5c_branch2a         (Conv2D)\n",
            "bn5c_branch2a          (BatchNorm)\n",
            "res5c_branch2b         (Conv2D)\n",
            "bn5c_branch2b          (BatchNorm)\n",
            "res5c_branch2c         (Conv2D)\n",
            "bn5c_branch2c          (BatchNorm)\n",
            "fpn_c5p5               (Conv2D)\n",
            "fpn_c4p4               (Conv2D)\n",
            "fpn_c3p3               (Conv2D)\n",
            "fpn_c2p2               (Conv2D)\n",
            "fpn_p5                 (Conv2D)\n",
            "fpn_p2                 (Conv2D)\n",
            "fpn_p3                 (Conv2D)\n",
            "fpn_p4                 (Conv2D)\n",
            "In model:  rpn_model\n",
            "    rpn_conv_shared        (Conv2D)\n",
            "    rpn_class_raw          (Conv2D)\n",
            "    rpn_bbox_pred          (Conv2D)\n",
            "mrcnn_mask_conv1       (TimeDistributed)\n",
            "mrcnn_mask_bn1         (TimeDistributed)\n",
            "mrcnn_mask_conv2       (TimeDistributed)\n",
            "mrcnn_mask_bn2         (TimeDistributed)\n",
            "mrcnn_class_conv1      (TimeDistributed)\n",
            "mrcnn_class_bn1        (TimeDistributed)\n",
            "mrcnn_mask_conv3       (TimeDistributed)\n",
            "mrcnn_mask_bn3         (TimeDistributed)\n",
            "mrcnn_class_conv2      (TimeDistributed)\n",
            "mrcnn_class_bn2        (TimeDistributed)\n",
            "mrcnn_mask_conv4       (TimeDistributed)\n",
            "mrcnn_mask_bn4         (TimeDistributed)\n",
            "mrcnn_bbox_fc          (TimeDistributed)\n",
            "mrcnn_mask_deconv      (TimeDistributed)\n",
            "mrcnn_class_logits     (TimeDistributed)\n",
            "mrcnn_mask             (TimeDistributed)\n",
            "Epoch 1/1\n",
            "100/100 [==============================] - 668s 7s/step - loss: 2.6438 - rpn_class_loss: 0.3342 - rpn_bbox_loss: 0.9316 - mrcnn_class_loss: 0.1882 - mrcnn_bbox_loss: 0.6009 - mrcnn_mask_loss: 0.5889 - val_loss: 2.2340 - val_rpn_class_loss: 0.1460 - val_rpn_bbox_loss: 0.7830 - val_mrcnn_class_loss: 0.1911 - val_mrcnn_bbox_loss: 0.5572 - val_mrcnn_mask_loss: 0.5566\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uxKzym-d8_Qo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Evaluate model on few random images"
      ]
    },
    {
      "metadata": {
        "id": "ZZconxSH8_Qo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ecf069c3-f760-45e6-ec8e-59aaeb9cea91"
      },
      "cell_type": "code",
      "source": [
        "# select trained model \n",
        "dir_names = next(os.walk(model.model_dir))[1]\n",
        "key = config.NAME.lower()\n",
        "dir_names = filter(lambda f: f.startswith(key), dir_names)\n",
        "dir_names = sorted(dir_names)\n",
        "\n",
        "if not dir_names:\n",
        "    import errno\n",
        "    raise FileNotFoundError(\n",
        "        errno.ENOENT,\n",
        "        \"Could not find model directory under {}\".format(self.model_dir))\n",
        "    \n",
        "fps = []\n",
        "# Pick last directory\n",
        "for d in dir_names: \n",
        "    dir_name = os.path.join(model.model_dir, d)\n",
        "    # Find the last checkpoint\n",
        "    checkpoints = next(os.walk(dir_name))[2]\n",
        "    checkpoints = filter(lambda f: f.startswith(\"mask_rcnn\"), checkpoints)\n",
        "    checkpoints = sorted(checkpoints)\n",
        "    if not checkpoints:\n",
        "        print('No weight files in {}'.format(dir_name))\n",
        "    else: \n",
        "      \n",
        "      checkpoint = os.path.join(dir_name, checkpoints[-1])\n",
        "      fps.append(checkpoint)\n",
        "\n",
        "model_path = sorted(fps)[-1]\n",
        "print('Found model {}'.format(model_path))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found model /content/lesson3-data/logs/pneumonia20180827T1517/mask_rcnn_pneumonia_0001.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qRCfu7_O8_Qr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 953
        },
        "outputId": "9dcc0230-e4a9-4b12-ceef-cf9e32f49d0c"
      },
      "cell_type": "code",
      "source": [
        "class InferenceConfig(DetectorConfig):\n",
        "    GPU_COUNT = 1\n",
        "    IMAGES_PER_GPU = 1\n",
        "\n",
        "inference_config = InferenceConfig()\n",
        "inference_config.display()\n",
        "\n",
        "# Recreate the model in inference mode\n",
        "model = modellib.MaskRCNN(mode='inference', \n",
        "                          config=inference_config,\n",
        "                          model_dir=MODEL_DIR)\n",
        "\n",
        "# Load trained weights (fill in path to trained weights here)\n",
        "assert model_path != \"\", \"Provide path to trained weights\"\n",
        "print(\"Loading weights from \", model_path)\n",
        "model.load_weights(model_path, by_name=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Configurations:\n",
            "BACKBONE                       resnet50\n",
            "BACKBONE_STRIDES               [4, 8, 16, 32, 64]\n",
            "BATCH_SIZE                     1\n",
            "BBOX_STD_DEV                   [0.1 0.1 0.2 0.2]\n",
            "COMPUTE_BACKBONE_SHAPE         None\n",
            "DETECTION_MAX_INSTANCES        3\n",
            "DETECTION_MIN_CONFIDENCE       0.9\n",
            "DETECTION_NMS_THRESHOLD        0.1\n",
            "FPN_CLASSIF_FC_LAYERS_SIZE     1024\n",
            "GPU_COUNT                      1\n",
            "GRADIENT_CLIP_NORM             5.0\n",
            "IMAGES_PER_GPU                 1\n",
            "IMAGE_MAX_DIM                  64\n",
            "IMAGE_META_SIZE                14\n",
            "IMAGE_MIN_DIM                  64\n",
            "IMAGE_MIN_SCALE                0\n",
            "IMAGE_RESIZE_MODE              square\n",
            "IMAGE_SHAPE                    [64 64  3]\n",
            "LEARNING_MOMENTUM              0.9\n",
            "LEARNING_RATE                  0.001\n",
            "LOSS_WEIGHTS                   {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0}\n",
            "MASK_POOL_SIZE                 14\n",
            "MASK_SHAPE                     [28, 28]\n",
            "MAX_GT_INSTANCES               3\n",
            "MEAN_PIXEL                     [123.7 116.8 103.9]\n",
            "MINI_MASK_SHAPE                (56, 56)\n",
            "NAME                           pneumonia\n",
            "NUM_CLASSES                    2\n",
            "POOL_SIZE                      7\n",
            "POST_NMS_ROIS_INFERENCE        1000\n",
            "POST_NMS_ROIS_TRAINING         200\n",
            "ROI_POSITIVE_RATIO             0.33\n",
            "RPN_ANCHOR_RATIOS              [0.5, 1, 2]\n",
            "RPN_ANCHOR_SCALES              (32, 64)\n",
            "RPN_ANCHOR_STRIDE              1\n",
            "RPN_BBOX_STD_DEV               [0.1 0.1 0.2 0.2]\n",
            "RPN_NMS_THRESHOLD              0.7\n",
            "RPN_TRAIN_ANCHORS_PER_IMAGE    16\n",
            "STEPS_PER_EPOCH                100\n",
            "TOP_DOWN_PYRAMID_SIZE          32\n",
            "TRAIN_BN                       False\n",
            "TRAIN_ROIS_PER_IMAGE           16\n",
            "USE_MINI_MASK                  True\n",
            "USE_RPN_ROIS                   True\n",
            "VALIDATION_STEPS               50\n",
            "WEIGHT_DECAY                   0.0001\n",
            "\n",
            "\n",
            "Loading weights from  /content/lesson3-data/logs/pneumonia20180827T1517/mask_rcnn_pneumonia_0001.h5\n",
            "Re-starting from epoch 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "IdQYgrSv8_Qu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def show_randoms(dataset):\n",
        "    fig = plt.figure(figsize=(10,20))\n",
        "\n",
        "    num_imgs = 3 \n",
        "    for i in range(num_imgs):\n",
        "\n",
        "        image_id = random.choice(dataset.image_ids)\n",
        "        original_image, image_meta, gt_class_id, gt_bbox, gt_mask =\\\n",
        "            modellib.load_image_gt(dataset, inference_config, \n",
        "                                   image_id, use_mini_mask=False)\n",
        "     \n",
        "        plt.subplot(num_imgs, 2, 2*i + 1)\n",
        "        visualize.display_instances(original_image, gt_bbox, gt_mask, gt_class_id, \n",
        "                                    dataset.class_names,\n",
        "                                    colors=get_colors_for_class_ids(gt_class_id), ax=fig.axes[-1])\n",
        "        plt.title('Ground Truth')\n",
        "        \n",
        "        plt.subplot(num_imgs, 2, 2*i + 2)\n",
        "        \n",
        "        # turn off verbose if you don't want debug messages \n",
        "        results = model.detect([original_image],verbose=1)\n",
        "        r = results[0]\n",
        "        visualize.display_instances(original_image, r['rois'], r['masks'], r['class_ids'], \n",
        "                                    dataset.class_names, r['scores'], \n",
        "                                    colors=get_colors_for_class_ids(r['class_ids']), ax=fig.axes[-1])\n",
        "        plt.title('Prediction')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5TBwjOQ48_Qw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def get_colors_for_class_ids(class_ids):\n",
        "    colors = []\n",
        "    for class_id in class_ids:\n",
        "        if class_id == 1:\n",
        "            colors.append((.941, .204, .204))\n",
        "    return colors"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "j4_uHSt48_Qy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1516
        },
        "outputId": "32ee49c5-9caf-488d-d2e5-d1f0732552db"
      },
      "cell_type": "code",
      "source": [
        "show_randoms(dataset_val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "*** No instances to display *** \n",
            "\n",
            "Processing 1 images\n",
            "image                    shape: (64, 64, 3)           min:    6.00000  max:  242.00000  uint8\n",
            "molded_images            shape: (1, 64, 64, 3)        min: -117.70000  max:  138.10000  float64\n",
            "image_metas              shape: (1, 14)               min:    0.00000  max:   64.00000  int64\n",
            "anchors                  shape: (1, 960, 4)           min:   -0.71833  max:    1.59135  float32\n",
            "Processing 1 images\n",
            "image                    shape: (64, 64, 3)           min:    0.00000  max:  254.00000  uint8\n",
            "molded_images            shape: (1, 64, 64, 3)        min: -123.70000  max:  150.10000  float64\n",
            "image_metas              shape: (1, 14)               min:    0.00000  max:   64.00000  int64\n",
            "anchors                  shape: (1, 960, 4)           min:   -0.71833  max:    1.59135  float32\n",
            "\n",
            "*** No instances to display *** \n",
            "\n",
            "Processing 1 images\n",
            "image                    shape: (64, 64, 3)           min:    0.00000  max:  247.00000  uint8\n",
            "molded_images            shape: (1, 64, 64, 3)        min: -123.70000  max:  143.10000  float64\n",
            "image_metas              shape: (1, 14)               min:    0.00000  max:   64.00000  int64\n",
            "anchors                  shape: (1, 960, 4)           min:   -0.71833  max:    1.59135  float32\n",
            "\n",
            "*** No instances to display *** \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FbTZFA+0Xa/kBAOa5ax57TNc89tjFrgZaHB0qAMC8dvmhQ7r80KGLXY2Wc3Lx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hwuW7lyZdFtLr74Yud8rOxnPgee34IFC4rOj0ObZkb1ONuv1PDwqN0qEoqa1PYrmw1e8chkMs59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\nH6gkSZI6rd2U3+HDh1v74MGDrc1KhxS/pqib8Suj1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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f0c69dc89e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}
